OpenRaiser/Intern-Atlas huggingface dataset:
Herd Behavior: Investigating Peer Influence in LLM-based Multi-Agent Systems
Venue: arXiv cs.MA 2025
Year: 2025

Recent advancements in Large Language Models (LLMs) have enabled the emergence of multi-agent systems where LLMs interact, collaborate, and make decisions in shared environments. While individual model behavior has been extensively studied, the dynamics of peer influence in such systems remain underexplored. In this paper, we investigate herd behavior, the tendency of agents to align their outputs with those of their peers, within LLM-based multi-agent interactions. We present a series of controlled experiments that reveal how herd behaviors are shaped by multiple factors. First, we show that the gap between self-confidence and perceived confidence in peers significantly impacts an agent's likelihood to conform. Second, we find that the format in which peer information is presented plays a critical role in modulating the strength of herd behavior. Finally, we demonstrate that the degree of herd behavior can be systematically controlled, and that appropriately calibrated herd tendencies can enhance collaborative outcomes. These findings offer new insights into the social dynamics of LLM-based systems and open pathways for designing more effective and adaptive multi-agent collaboration frameworks.
Geometric Feature Prompting of Image Segmentation Models
Venue: arXiv cs.CV 2025
Year: 2025

Advances in machine learning, especially the introduction of transformer architectures and vision transformers, have led to the development of highly capable computer vision foundation models. The segment anything model (known colloquially as SAM and more recently SAM 2), is a highly capable foundation model for segmentation of natural images and has been further applied to medical and scientific image segmentation tasks. SAM relies on prompts -- points or regions of interest in an image -- to generate associated segmentations. In this manuscript we propose the use of a geometrically motivated prompt generator to produce prompt points that are colocated with particular features of interest. Focused prompting enables the automatic generation of sensitive and specific segmentations in a scientific image analysis task using SAM with relatively few point prompts. The image analysis task examined is the segmentation of plant roots in rhizotron or minirhizotron images, which has historically been a difficult task to automate. Hand annotation of rhizotron images is laborious and often subjective; SAM, initialized with GeomPrompt local ridge prompts has the potential to dramatically improve rhizotron image processing. The authors have concurrently released an open source software suite called geomprompt https://pypi.org/project/geomprompt/ that can produce point prompts in a format that enables direct integration with the segment-anything package.
AutoSGD: Automatic Learning Rate Selection for Stochastic Gradient Descent
Venue: arXiv cs.LG 2025
Year: 2025

The learning rate is an important tuning parameter for stochastic gradient descent (SGD) and can greatly influence its performance. However, appropriate selection of a learning rate schedule across all iterations typically requires a non-trivial amount of user tuning effort. To address this, we introduce AutoSGD: an SGD method that automatically determines whether to increase or decrease the learning rate at a given iteration and then takes appropriate action. We introduce theory supporting the convergence of AutoSGD, along with its deterministic counterpart for standard gradient descent. Empirical results suggest strong performance of the method on a variety of traditional optimization problems and machine learning tasks.
Think Before You Diffuse: Infusing Physical Rules into Video Diffusion
Venue: arXiv cs.CV 2025
Year: 2025

Recent video diffusion models have demonstrated their great capability in generating visually-pleasing results, while synthesizing the correct physical effects in generated videos remains challenging. The complexity of real-world motions, interactions, and dynamics introduce great difficulties when learning physics from data. In this work, we propose DiffPhy, a generic framework that enables physically-correct and photo-realistic video generation by fine-tuning a pre-trained video diffusion model. Our method leverages large language models (LLMs) to infer rich physical context from the text prompt. To incorporate this context into the video diffusion model, we use a multimodal large language model (MLLM) to verify intermediate latent variables against the inferred physical rules, guiding the gradient updates of model accordingly. Textual output of LLM is transformed into continuous signals. We then formulate a set of training objectives that jointly ensure physical accuracy and semantic alignment with the input text. Additionally, failure facts of physical phenomena are corrected via attention injection. We also establish a high-quality physical video dataset containing diverse phyiscal actions and events to facilitate effective finetuning. Extensive experiments on public benchmarks demonstrate that DiffPhy is able to produce state-of-the-art results across diverse physics-related scenarios. Our project page is available at https://bwgzk-keke.github.io/DiffPhy/.
On Reconfigurable Bisimulation, with an Application to the Distributed Synthesis Problem
Venue: arXiv cs.LO 2025
Year: 2025

We consider the problem of distributing a centralised transition system to a set of asynchronous agents recognising the same language. Existing solutions are either manual or involve a huge explosion in the number of states from the centralised system. The difficulty arises from the need to keep a rigid communication scheme, specifying a fixed mapping from events to those who can participate in them. Thus, individual agents need to memorise seen events and their order to dynamically compare their knowledge with others when communicating. To bypass this, we rely on reconfigurable communication: agents decide locally ``by-need'' when to participate or discard specific events during execution while not impacting the progress of the joint computation. Our distribution relies on a novel notion of Parametric Reconfigurable Bisimulation, that identifies the only required participations. We show how to compute this bisimulation and that such minimisation produces a joint system that is bisimilar to the original centralised one. We use a case study to show its effectiveness by producing agents that are much smaller than the centralised system and jointly perform the same computations. As a notable application, we use this distribution in order to allow for distributed synthesis from global specifications. In this case, rigid communication leads to undecidability, which is bypassed by our ability to dynamically prune communications.
Make Planning Research Rigorous Again!
Venue: arXiv cs.AI 2025
Year: 2025

In over sixty years since its inception, the field of planning has made significant contributions to both the theory and practice of building planning software that can solve a never-before-seen planning problem. This was done through established practices of rigorous design and evaluation of planning systems. It is our position that this rigor should be applied to the current trend of work on planning with large language models. One way to do so is by correctly incorporating the insights, tools, and data from the automated planning community into the design and evaluation of LLM-based planners. The experience and expertise of the planning community are not just important from a historical perspective; the lessons learned could play a crucial role in accelerating the development of LLM-based planners. This position is particularly important in light of the abundance of recent works that replicate and propagate the same pitfalls that the planning community has encountered and learned from. We believe that avoiding such known pitfalls will contribute greatly to the progress in building LLM-based planners and to planning in general.
Real-World Deployment of Cloud Autonomous Mobility System Using 5G Networks for Outdoor and Indoor Environments
Venue: arXiv cs.RO 2025
Year: 2025

The growing complexity of both outdoor and indoor mobility systems demands scalable, cost-effective, and reliable perception and communication frameworks. This work presents the real-world deployment and evaluation of a Cloud Autonomous Mobility (CAM) system that leverages distributed sensor nodes connected via 5G networks, which integrates LiDAR- and camera-based perception at infrastructure units, cloud computing for global information fusion, and Ultra-Reliable Low Latency Communications (URLLC) to enable real-time situational awareness and autonomous operation. The CAM system is deployed in two distinct environments: a dense urban roundabout and a narrow indoor hospital corridor. Field experiments show improved traffic monitoring, hazard detection, and asset management capabilities. The paper also discusses practical deployment challenges and shares key insights for scaling CAM systems. The results highlight the potential of cloud-based infrastructure perception to advance both outdoor and indoor intelligent transportation systems.
AMSFL: Adaptive Multi-Step Federated Learning via Gradient Difference-Based Error Modeling
Venue: arXiv cs.LG 2025
Year: 2025

Federated learning faces critical challenges in balancing communication efficiency and model accuracy. One key issue lies in the approximation of update errors without incurring high computational costs. In this paper, we propose a lightweight yet effective method called Gradient Difference Approximation (GDA), which leverages first-order information to estimate local error trends without computing the full Hessian matrix. The proposed method forms a key component of the Adaptive Multi-Step Federated Learning (AMSFL) framework and provides a unified error modeling strategy for large-scale multi-step adaptive training environments.
How Soft Skills Shape First-Year Success in Higher Education
Venue: arXiv cs.CY 2025
Year: 2025

Soft skills are critical for academic and professional success, but are often neglected in early-stage technical curricula. This paper presents a semi-isolated teaching intervention aimed at fostering study ability and key soft skills-communication, collaboration, and project management-among first-year computer science students. The elective seminar Soft Skills and Tools for Studies and Career in IT was alongside a mandatory team-based programming course. We analyze project outcomes and student experiences across three cohorts across three groups: students who attended the seminar, students who teamed up with a seminar attendee, and students with no exposure to the seminar. Results show that seminar participants performed significantly better in individual presentations and team projects. Qualitative feedback further indicates improved team dynamics and study preparedness. Although self-assessed collaboration and communication did not reach statistical significance, consistent trends suggest that early soft skills training enhances academic integration. We recommend embedding such interventions early in technical study programs to support the transition into university life.
MedBridge: Bridging Foundation Vision-Language Models to Medical Image Diagnosis in Chest X-Ray
Venue: arXiv cs.CV 2025
Year: 2025

Recent vision-language foundation models deliver state-of-the-art results in natural image classification, but falter in medical images due to pronounced domain shifts. Training a medical foundation model also requires substantial resources, including extensive annotated data and high computational capacity. To bridge this gap with minimal overhead, we introduce MedBridge, a lightweight multimodal adaptation framework that flexibly re-purposes arbitrary pre-trained foundation VLMs for medical image diagnosis. MedBridge comprises three novel core components. First, a Focal Sampling module that subsamples and extracts high-resolution local regions to capture subtle pathological features, compensating for the limited input resolution of foundation VLMs. Second, a Query-Encoder model with a small set of learnable queries to align the feature maps of frozen VLMs with medical semantics, without requiring retraining of the backbone layers. Third, a Mixture of Experts mechanism, driven by learnable queries, harnesses the complementary strength of various VLMs to maximize diagnostic performance. We evaluate MedBridge on five chest radiograph benchmarks in three key adaptation tasks, demonstrating its superior performance in both cross-domain and in-domain adaptation settings under varying levels of training data availability. MedBridge achieved an improvement of 6-15% in AUC compared to state-of-the-art VLM adaptation methods in multi-label thoracic disease diagnosis, underscoring its effectiveness in leveraging diverse foundation models for accurate and data-efficient medical diagnosis. Our project and code are available at https://github.com/ai-med/MedBridge.
Network classification through random walks
Venue: arXiv cs.SI 2025
Year: 2025

Network models have been widely used to study diverse systems and analyze their dynamic behaviors. Given the structural variability of networks, an intriguing question arises: Can we infer the type of system represented by a network based on its structure? This classification problem involves extracting relevant features from the network. Existing literature has proposed various methods that combine structural measurements and dynamical processes for feature extraction. In this study, we introduce a novel approach to characterize networks using statistics from random walks, which can be particularly informative about network properties. We present the employed statistical metrics and compare their performance on multiple datasets with other state-of-the-art feature extraction methods. Our results demonstrate that the proposed method is effective in many cases, often outperforming existing approaches, although some limitations are observed across certain datasets.
Bridging the Narrative Divide: Cross-Platform Discourse Networks in Fragmented Ecosystems
Venue: arXiv cs.SI 2025
Year: 2025

Political discourse has grown increasingly fragmented across different social platforms, making it challenging to trace how narratives spread and evolve within such a fragmented information ecosystem. Reconstructing social graphs and information diffusion networks is challenging, and available strategies typically depend on platform-specific features and behavioral signals which are often incompatible across systems and increasingly restricted. To address these challenges, we present a platform-agnostic framework that allows to accurately and efficiently reconstruct the underlying social graph of users' cross-platform interactions, based on discovering latent narratives and users' participation therein. Our method achieves state-of-the-art performance in key network-based tasks: information operation detection, ideological stance prediction, and cross-platform engagement prediction$\unicode{x2013}$$\unicode{x2013}$while requiring significantly less data than existing alternatives and capturing a broader set of users. When applied to cross-platform information dynamics between Truth Social and X (formerly Twitter), our framework reveals a small, mixed-platform group of $\textit{bridge users}$, comprising just 0.33% of users and 2.14% of posts, who introduce nearly 70% of $\textit{migrating narratives}$ to the receiving platform. These findings offer a structural lens for anticipating how narratives traverse fragmented information ecosystems, with implications for cross-platform governance, content moderation, and policy interventions.
GMU Systems for the IWSLT 2025 Low-Resource Speech Translation Shared Task
Venue: arXiv cs.CL 2025
Year: 2025

This paper describes the GMU systems for the IWSLT 2025 low-resource speech translation shared task. We trained systems for all language pairs, except for Levantine Arabic. We fine-tuned SeamlessM4T-v2 for automatic speech recognition (ASR), machine translation (MT), and end-to-end speech translation (E2E ST). The ASR and MT models are also used to form cascaded ST systems. Additionally, we explored various training paradigms for E2E ST fine-tuning, including direct E2E fine-tuning, multi-task training, and parameter initialization using components from fine-tuned ASR and/or MT models. Our results show that (1) direct E2E fine-tuning yields strong results; (2) initializing with a fine-tuned ASR encoder improves ST performance on languages SeamlessM4T-v2 has not been trained on; (3) multi-task training can be slightly helpful.
P-DROP: Poisson-Based Dropout for Graph Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

Over-smoothing remains a major challenge in Graph Neural Networks (GNNs), where repeated message passing causes node representations to converge and lose discriminative power. To address this, we propose a novel node selection strategy based on Poisson processes, introducing stochastic but structure-aware updates. Specifically, we equip each node with an independent Poisson clock, enabling asynchronous and localized updates that preserve structural diversity. We explore two applications of this strategy: as a replacement for dropout-based regularization and as a dynamic subgraph training scheme. Experimental results on standard benchmarks (Cora, Citeseer, Pubmed) demonstrate that our Poisson-based method yields competitive or improved accuracy compared to traditional Dropout, DropEdge, and DropNode approaches, particularly in later training stages.
From Directions to Cones: Exploring Multidimensional Representations of Propositional Facts in LLMs
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) exhibit strong conversational abilities but often generate falsehoods. Prior work suggests that the truthfulness of simple propositions can be represented as a single linear direction in a model's internal activations, but this may not fully capture its underlying geometry. In this work, we extend the concept cone framework, recently introduced for modeling refusal, to the domain of truth. We identify multi-dimensional cones that causally mediate truth-related behavior across multiple LLM families. Our results are supported by three lines of evidence: (i) causal interventions reliably flip model responses to factual statements, (ii) learned cones generalize across model architectures, and (iii) cone-based interventions preserve unrelated model behavior. These findings reveal the richer, multidirectional structure governing simple true/false propositions in LLMs and highlight concept cones as a promising tool for probing abstract behaviors.
HDRSDR-VQA: A Subjective Video Quality Dataset for HDR and SDR Comparative Evaluation
Venue: arXiv cs.CV 2025
Year: 2025

We introduce HDRSDR-VQA, a large-scale video quality assessment dataset designed to facilitate comparative analysis between High Dynamic Range (HDR) and Standard Dynamic Range (SDR) content under realistic viewing conditions. The dataset comprises 960 videos generated from 54 diverse source sequences, each presented in both HDR and SDR formats across nine distortion levels. To obtain reliable perceptual quality scores, we conducted a comprehensive subjective study involving 145 participants and six consumer-grade HDR-capable televisions. A total of over 22,000 pairwise comparisons were collected and scaled into Just-Objectionable-Difference (JOD) scores. Unlike prior datasets that focus on a single dynamic range format or use limited evaluation protocols, HDRSDR-VQA enables direct content-level comparison between HDR and SDR versions, supporting detailed investigations into when and why one format is preferred over the other. The open-sourced part of the dataset is publicly available to support further research in video quality assessment, content-adaptive streaming, and perceptual model development.
UniMoGen: Universal Motion Generation
Venue: arXiv cs.CV 2025
Year: 2025

Motion generation is a cornerstone of computer graphics, animation, gaming, and robotics, enabling the creation of realistic and varied character movements. A significant limitation of existing methods is their reliance on specific skeletal structures, which restricts their versatility across different characters. To overcome this, we introduce UniMoGen, a novel UNet-based diffusion model designed for skeleton-agnostic motion generation. UniMoGen can be trained on motion data from diverse characters, such as humans and animals, without the need for a predefined maximum number of joints. By dynamically processing only the necessary joints for each character, our model achieves both skeleton agnosticism and computational efficiency. Key features of UniMoGen include controllability via style and trajectory inputs, and the ability to continue motions from past frames. We demonstrate UniMoGen's effectiveness on the 100style dataset, where it outperforms state-of-the-art methods in diverse character motion generation. Furthermore, when trained on both the 100style and LAFAN1 datasets, which use different skeletons, UniMoGen achieves high performance and improved efficiency across both skeletons. These results highlight UniMoGen's potential to advance motion generation by providing a flexible, efficient, and controllable solution for a wide range of character animations.
Joint$λ$: Orchestrating Serverless Workflows on Jointcloud FaaS Systems
Venue: arXiv cs.DC 2025
Year: 2025

Existing serverless workflow orchestration systems are predominantly designed for a single-cloud FaaS system, leading to vendor lock-in. This restricts performance optimization, cost reduction, and availability of applications. However, orchestrating serverless workflows on Jointcloud FaaS systems faces two main challenges: 1) Additional overhead caused by centralized cross-cloud orchestration; and 2) A lack of reliable failover and fault-tolerant mechanisms for cross-cloud serverless workflows. To address these challenges, we propose Joint$λ$, a distributed runtime system designed to orchestrate serverless workflows on multiple FaaS systems without relying on a centralized orchestrator. Joint$λ$ introduces a compatibility layer, Backend-Shim, leveraging inter-cloud heterogeneity to optimize makespan and reduce costs with on-demand billing. By using function-side orchestration instead of centralized nodes, it enables independent function invocations and data transfers, reducing cross-cloud communication overhead. For high availability, it ensures exactly-once execution via datastores and failover mechanisms for serverless workflows on Jointcloud FaaS systems. We validate Joint$λ$ on two heterogeneous FaaS systems, AWS and ALiYun, with four workflows. Compared to the most advanced commercial orchestration services for single-cloud serverless workflows, Joint$λ$ reduces up to 3.3$\times$ latency, saving up to 65\% cost. Joint$λ$ is also faster than the state-of-the-art orchestrators for cross-cloud serverless workflows up to 4.0$\times$, reducing up to 4.5$\times$ cost and providing strong execution guarantees.